
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "181"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 91 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 87 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.356491	0.166488	1.708732	4.666447	-0.755969	0.357847	0.928121	4.467881	0.7829	0.7915	0.2502	12.142091	11.774303
2459593	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.552567	1.204124	2.046961	5.457594	-0.791428	0.333289	-0.169969	1.749484	0.7468	0.7422	0.2507	5.924591	5.207128
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.037461	-1.131036	-0.183376	0.697888	1.237330	-0.152436	1.323809	0.220350	0.0460	0.0356	-0.0011	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.019333	1.923174	2.393213	6.240259	-0.867711	0.636116	-0.846367	2.136028	0.6768	0.6876	0.3750	5.399116	5.081372
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.497437	3.333420	1.947542	6.020011	-0.399845	0.478461	-0.206482	2.235724	0.6784	0.6856	0.3676	5.851168	5.698536
2459589	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.239985	1.882622	1.723744	5.387593	0.277193	1.271446	-0.159542	2.522884	0.7111	0.7095	0.2895	6.168761	5.348105
2459588	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.408921	2.147333	2.668634	5.673148	0.309711	0.083907	0.750070	1.367685	0.7874	0.7732	0.2186	7.843507	6.665496
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	353.521423	357.821937	inf	inf	50.943647	102.315746	364.744102	589.195810	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	7.03%	0.00%	0.178642	1.078711	-0.604558	-0.643049	-0.553246	-1.028997	0.413181	1.371197	0.6570	0.6676	0.3721	1.507630	1.518344
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.458022	-1.281456	0.076380	0.377507	1.300515	-0.043132	1.392746	2.438529	0.0433	0.0324	0.0066	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.272517	1.588775	4.151902	-0.766677	9.076960	-0.261412	2.091496	3.107357	0.6178	0.6237	0.3764	0.000000	0.000000
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	3.23%	0.00%	-0.219690	0.971982	1.809773	0.212127	-0.943480	-0.926241	-0.966789	1.537008	0.6812	0.6897	0.3879	1.417077	1.454225
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	2.70%	0.00%	-0.053308	2.215839	1.955367	0.332194	-0.694361	-0.622895	-0.943838	1.546076	0.6829	0.6905	0.3907	1.426892	1.420160
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	0.00%	-0.269592	3.176214	1.513435	-0.024879	-0.269352	-0.430335	-0.749101	2.021638	0.6892	0.6947	0.3857	1.580777	1.545800
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	-0.336683	1.933868	2.374543	0.183257	-0.259591	-0.493136	-0.543399	1.337768	0.6820	0.6891	0.3849	1.345273	1.389301
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	97.31%	2.69%	-0.343256	1.438327	2.796377	0.794373	-0.490354	-0.597573	-1.028030	3.636104	0.6775	0.6842	0.3869	0.000000	0.000000
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.662887	-0.043013	0.007127	-0.759391	1.886476	-0.290116	-1.036968	1.549480	0.0546	0.0482	-0.0005	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	5.41%	15.68%	0.056379	3.141161	1.527245	0.236419	-0.317527	-0.143132	-0.767712	1.646119	0.6754	0.6808	0.3924	1.549742	1.444794
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	0.00%	-0.740179	0.768998	1.014268	0.331190	-0.393211	-0.570042	0.779259	0.406531	0.7577	0.7628	0.3314	1.602940	1.567927
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	-0.352972	1.710660	0.821218	0.241727	0.436800	-0.062486	0.479218	1.349519	0.6971	0.7038	0.3866	1.548231	1.442461
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	-0.209411	1.576139	2.771025	0.300949	-0.464403	-0.732312	-0.877972	3.049242	0.7010	0.7087	0.3844	1.504722	1.500958
2459572	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.439980	1.759540	1.113069	0.291171	-0.603858	-0.640406	-0.812420	4.573306	0.7057	0.7134	0.3853	4.673560	4.092312
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	nan	nan
2459570	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	-0.867738	-0.608601	-0.502201	-0.439798	-0.764326	-0.473136	-1.017970	-0.502952	0.6879	0.6986	0.3574	1.432316	1.312194
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	0.074206	1.991342	1.119022	0.288873	0.676694	0.059278	0.541568	0.326395	0.7668	0.7722	0.2402	2.149890	1.829235
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	-0.029429	1.711375	1.638353	0.278700	-0.763553	-0.372757	-0.188632	1.702714	0.6942	0.7021	0.4018	1.486045	1.519594
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	-2.008718	-2.008718	-1.878795	-1.878795	-0.888145	-0.888145	-0.944419	-0.944419	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.00%	0.016014	1.997349	-1.904837	0.740683	1.264628	0.005793	-0.299952	1.497719	0.7089	0.7094	0.3987	1.548551	1.444347
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.142338	1.659687	0.814748	1.217193	-0.840743	-0.408891	-0.489422	2.176315	0.7064	0.7104	0.3927	1.759342	1.586048
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.493903	1.526713	-1.652166	0.471609	-0.211869	0.652411	-0.260005	0.976439	0.7068	0.7136	0.3875	1.722179	1.503470
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.345771	1.404777	-1.753634	0.209390	-0.584598	0.284259	-0.790348	1.584947	0.7044	0.7131	0.3892	1.628239	1.509711
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.124264	1.445270	-1.559215	0.273152	-0.045295	0.260998	-0.427855	1.555139	0.7053	0.7129	0.3988	1.626926	1.476085
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.740681	0.976717	-1.794811	0.261967	-0.271827	0.033651	-0.484665	1.345568	0.7002	0.7074	0.3921	1.587963	1.464885
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	13.44%	0.54%	0.167509	2.234460	2.333613	0.322493	-0.693077	-0.644987	-0.907929	2.921503	0.7060	0.7073	0.3921	1.679046	1.544723
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0560	0.0432	-0.0022	nan	nan
2459556	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.318591	1.925795	2.457289	0.674222	-0.435627	-0.325004	-1.431484	4.965529	0.6990	0.7028	0.3949	3.799939	3.236738
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7091	0.7138	0.4007	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7032	0.7085	0.4007	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.194788	1.877602	3.341287	0.142298	0.654793	-1.725713	-0.922515	2.162288	0.7142	0.7144	0.3946	0.876091	0.919236
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.302614	1.855536	-2.002472	0.306016	-0.439438	0.360109	-0.415112	1.997265	0.7082	0.7103	0.3945	1.618858	1.488812
2459550	digital_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0511	0.0461	-0.0003	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	8.11%	0.00%	-0.279048	1.634439	2.004864	0.153995	-0.390789	-0.773451	-0.920687	2.730814	0.7000	0.6997	0.4082	1.763521	1.548273
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	0.046948	1.524211	1.384706	0.915964	1.049968	-0.664257	0.167596	2.181025	0.7488	0.7409	0.3704	1.707773	1.486632
2459541	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.00%	-0.350873	1.757691	-1.898619	0.209216	-2.051766	1.012533	-0.433040	1.103164	0.7568	0.7490	0.3497	1.576097	1.454619
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.220393	3.724437	4.168546	0.736720	0.893197	-0.123875	-0.882767	3.267733	0.7357	0.7333	0.4229	5.462085	4.707088
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.064670	1.193042	-1.611018	0.162976	-1.564815	1.385416	0.013964	1.495027	0.7924	0.7712	0.3637	1.643304	1.484017
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.00%	0.015125	1.358183	-1.508843	0.025063	-0.892346	1.504775	-0.105808	1.164135	0.7829	0.7587	0.3633	1.479483	1.468830
2459533	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.138247	2.285674	2.420525	-0.141149	-0.244013	-0.686937	1.184349	4.407600	0.7204	0.7188	0.4100	2.960050	2.602930
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	0.00%	0.259842	2.359673	3.309989	0.630831	-0.292346	-0.143567	0.086921	2.766933	0.7303	0.7290	0.4019	1.524628	1.488170
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	4.73%	0.00%	-0.511568	1.325441	-1.551594	0.270731	-0.729719	0.477181	-0.361758	1.718421	0.7382	0.7293	0.4028	1.699308	1.486151
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.051655	1.147971	2.598376	-0.125806	2.698699	1.438155	-0.707696	1.478255	0.9044	0.9300	0.3257	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	20.41%	0.00%	-0.200615	2.217421	3.184022	0.367959	0.833592	1.409409	-1.498821	2.187495	nan	nan	nan	2.156642	1.910849
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.259772	2.783727	3.339770	0.553923	0.507173	2.623100	0.221528	5.073222	0.6587	0.6510	0.3654	3.947669	4.140810
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	1.064376	0.048460	-0.154541	-0.627933	6.264714	0.697561	-0.771539	1.872548	0.0695	0.0699	0.0018	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.115841	0.188083	1.820370	-0.371894	1.265377	-0.435679	0.804976	-0.161647	0.9106	0.9347	0.1979	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.164784	1.248877	3.054239	0.908375	-1.004587	0.494843	-0.904779	3.127533	0.8875	0.8421	0.2279	2.692585	1.808897
2459503	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.515682	0.925495	3.097157	0.052481	5.139708	0.719155	-0.651122	1.255464	0.8774	0.8639	0.4573	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 181 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 181, Node 13:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶